**Algorithm**

An algorithm is a sequence of instructions to carry out a given operation. This can be a convenient way to play a compressed video file, for example. They are multiplying two numbers or a complicated process. Search engines use original algorithms to display the most appropriate results for particular queries in their search index. Also, algorithms are typically built as functions in computer programming. A broader application may reference this functionality as small programmers. A collection of capabilities that each uses an algorithm to reduce various image file formats can be used in an image viewing program. Picture editing software can involve image processing algorithms. Algorithms are often transparent and are used for estimation, data processing, automating reasoning and other tasks requirements. An algorithm can measure a function as an efficient process in a limited duration and a well-defined formal language.

**Binary Search in Computer Science**

Binary search is a fast, runtime complex search algorithm (log n). It functions on the splitting and conquering principle. The data set should be sorted to ensure that this algorithm operates appropriately. Binary search is based on a comparison of the middle portion of the set for a given document. The Index of the object is returned if a match exists. If the middle item is more significant than the component, it is looked upon as the middle element’s left in the sub-array. In the sub-array on the right of the medium object, **computer science assignment** the item would otherwise be scanned. This operation proceeds on the subarray until the subarray size is reduced to zero (Odokuma & Owolabi, 2016, pp. 116-122).

**Working of a Binary Search Algorithm**

First of all, a binary search algorithm still operates in a sorted array. The first logical move is then to figure out the list. The median is tested for the desired value after sorting.

- If the desired value matches the value of the central Index, the Index is returned as a response.

- When the target is less than the list’s central index value, the list’s right side is overlooked.

- If the value users seek higher than the value of the central Index, the left half is deactivated.

- On short lists, the procedure is replicated until the desired value is found.

Binary search is widely considered to be the optimal search algorithm for ordered and unsorted arrays. The binary search takes little account of data to form in the structure. However, it can be useful for more accurate filtering (whether data structure elements grow linearly, exponentially, or with various variations). There are two other search methods, interpolation, and hash table, which are slowly stronger than binary search in some cases. The position or Index of the target element is similar to the presented search for algorithms, and the search space is limited to the portion before or after the approximate Index. It takes the same steps before the goal feature is identified. Searching for exclamation works well if the data elements are uniformly distributed but fail when factors exponentially increase or increase with very different variations (Mehmood, 2019)**.**

**Selection Sort**

** ** Selection sort is a basic comparison-based sorting algorithm. The selection sort algorithm begins to find the smallest element in the list. Then the smallest element in the first place is shared with the element. Following this first step, the algorithm attempts to pick in each step the smallest element in the unsorted part of the array. This smaller selected element is shared with the element in the unsorted portion of the sort. This method continues until there are no unelected objects in the collection (Furat, 2016, pp. 327-329). The selection sort algorithm spends most of its time to find the smallest element in the unsorted part of the array [1, 2, 3, 7, 8, 13, and 15].

**Linear Regression algorithm in Machine Learning**

Machine learning uses programmed algorithms to obtain and interpret input data appropriately for predicting output values. When new data are given to these algorithms, they learn and refine their processes to enhance the presentation and, over time, to build ‘intelligence.’ On the other hand, machine learning has played a prominent role in our everyday lives, **Machine Learning Assignment** being a branch of artificial intelligence (AI). Data scientists and engineers in diverse fields typically find their jobs easier and simpler by using machine learning algorithms.

Linear regression is known as supervised learning algorithms for learning machines. Regression is used where continuing values are expected; predictive values are used to project potential values based on history and meaning. In statistics, linear regression was commonly used to achieve the association of the numerical component input to output.

Linear regression is an exceptional model because it can be expressed very quickly. The photo is a linear equation with a set of input values (x) expected based on a set of input values (y). The input and output values are integers, above stated. The linear equation assigns the single input value or column, defined as a coefficient and expressed by the Greek capital letter Beta (B). A supplementary coefficient is often applied to provide the lines with an extra degree of freedom to travel up and down in a two-dimensional track and is commonly known as intercept or bias coefficient. For instance, y = B0 + B1*x is a simple regression problem (Gandhi, 2018).

Figure 3 Linear regression (Gandhi, 2018).

In Stock Prediction Analysis, the linear regression machine algorithm is used. Python software can quickly build machine learning algorithms. The first APPLE and TSLA stock details can be obtained from the web here (Rao, 2020, pp. 841-843).

For a particular explanatory variable, the Basic Linear Regression is called (i.e., a linear association between x and y) (Krishna, 2019).

*y = mx + c*

When y is the vector dependent, x is the independent variable, m is the line slope, and c is the y-intercept( *value of **y** when x=0*)

**Calculate predicted values (***mx + c***)**

**(KNN) Algorithm**

The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve the following problems (Zhang, 2016, p. 218).

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